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Accurate identification of coal macrolithotypes is essential for reservoir characterization and guiding efficient exploration and development. However, the deep coal seams in the Ordos Basin feature complex geological conditions and strong spatial heterogeneity. Traditional prediction models exhibit low accuracy at the basin-wide scale, thereby constraining the fine-scale identification of macrolithotypes. This study proposes a machine learning prediction workflow driven by dual geological constraints: roof lithology and burial depth. Based on core samples and well-logging data, multiple machine learning models were constructed to evaluate and compare the performance of global models versus geologically partitioned models. The findings reveal that (1) Geologically partitioned modeling significantly outperforms global modeling, and the optimal prediction models vary distinctly under different geological backgrounds. (2) Within the depth and regional scope of this study, the distribution of macrolithotypes is dominated by sedimentary microfacies. Bright coal is most extensively developed beneath limestone roofs (accounting for 53.3%), corresponding to a low-lying mire facies with stable hydrodynamics, whereas semibright coal dominates beneath sandstone roofs. (3) A \u201cgeology-engineering\u201d dual sweet-spot evaluation paradigm is proposed: the \u201climestone roof + bright coal\u201d combination, characterized by exceptionally high gas content, serves as the premier geological resource sweet spot; conversely, the \u201csandstone roof + semibright coal\u201d combination, featuring a higher elastic modulus and stress stability, acts as an engineering stable-production sweet spot that ensures long-term fracture conductivity. This study provides a robust scientific basis for sweet-spot evaluation and tailored fracturing designs in deep CBM exploration.<\/jats:p>","DOI":"10.1021\/acsomega.6c05410","type":"journal-article","created":{"date-parts":[[2026,8,14]],"date-time":"2026-08-14T22:01:17Z","timestamp":1786744877000},"page":"50318-50333","source":"Crossref","is-referenced-by-count":0,"title":["Prediction and\nControlling Factors of Macrolithotypes\nin Deep Coal Reservoirs"],"prefix":"10.1021","volume":"11","author":[{"given":"Yingying","family":"Zhao","sequence":"first","affiliation":[{"name":"China University of Geosciences , , ,","place":["Beijing, China, 100083"]},{"name":"Peking University Ordos Research Institute of Energy , , ,","place":["Ordos, China, 017000"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6859-9687","authenticated-orcid":true,"given":"Bo","family":"Jiu","sequence":"additional","affiliation":[{"name":"China University of Geosciences , , ,","place":["Beijing, China, 100083"]},{"name":"Peking University Ordos Research Institute of Energy , , ,","place":["Ordos, China, 017000"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9623-2652","authenticated-orcid":true,"given":"Wenhui","family":"Huang","sequence":"additional","affiliation":[{"name":"China University of Geosciences , , ,","place":["Beijing, China, 100083"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhenguang","family":"Shang","sequence":"additional","affiliation":[{"name":"Peking University Ordos Research Institute of Energy , , ,","place":["Ordos, China, 017000"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Li","family":"Long","sequence":"additional","affiliation":[{"name":"Peking University Ordos Research Institute of Energy , , ,","place":["Ordos, China, 017000"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Linghao","family":"Huang","sequence":"additional","affiliation":[{"name":"China University of Geosciences , , ,","place":["Beijing, China, 100083"]},{"name":"Peking University Ordos Research Institute of Energy , , ,","place":["Ordos, China, 017000"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haonan","family":"Chen","sequence":"additional","affiliation":[{"name":"China University of Geosciences , , ,","place":["Beijing, China, 100083"]},{"name":"Peking University Ordos Research Institute of Energy , , ,","place":["Ordos, China, 017000"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"316","published-online":{"date-parts":[[2026,8,14]]},"reference":[{"key":"2026083119130997100_cit1","doi-asserted-by":"publisher","first-page":"777","DOI":"10.1080\/00206814.2017.1408034","article-title":"Resources and geology\nof coalbed methane in China: a review","volume":"60","author":"Qin","year":"2018","journal-title":"Int.\nGeol. 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Coal Geol."},{"key":"2026083119130997100_cit46","doi-asserted-by":"crossref","unstructured":"Chen, T.; Guestrin, C. In  XGBoost:\nA Scalable\nTree Boosting System, Proceedings of the 22nd ACM SIGKDD\nInternational Conference on Knowledge Discovery and Data Mining; ACM, 2016; pp 785\u2013794.","DOI":"10.1145\/2939672.2939785"},{"key":"2026083119130997100_cit47","doi-asserted-by":"publisher","DOI":"10.1016\/j.enggeo.2024.107696","article-title":"Characterization of\nnatural fracture development in coal reservoirs\nusing logging machine learning inversion, well test data and simulated\ngeostress analyses","volume":"341","author":"Wang","year":"2024","journal-title":"Eng. Geol."},{"key":"2026083119130997100_cit48","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-025-06762-1","article-title":"A granular\nXGBoost classification\nalgorithm","volume":"55","author":"Lan","year":"2025","journal-title":"Appl. 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